Taking the recursive neural network for the structure prediction described in [1]}[2]} as an example, the goal is to learn a function \(f:\mathcal {X}\rightarrow \mathcal {Y}\) , where \(\mathcal {Y}\)  is the set of all possible binary parse trees. An input \(x\in \mathcal {X}\)  consists of (a) a set of activation vectors which represent input elements such as image segments or words of a sentence, and (b) a symmetric adjacency matrix to define which elements can be merged.
